model{ ## priors omega ~ dunif(0,1) phi ~ dunif(0,1) r ~ dunif(0,1) p ~ dunif(0,1) ## likelihood for(i in 1:N){ x[i,1] ~ dbern(omega) for(j in 1:J) { mu.p[i,1,j] <- x[i,1]*p y[i,1,j] ~ dbern(mu.p[i,1,j]) } for(t in 2:T) { mu.phi[i,t] <- x[i,t-1]*phi z[i,t] ~ dbern(mu.phi[i,t]) mu.r[i,t] <- z[i,t] + (1-z[i,t])*r x[i,t] ~ dbern(mu.r[i,t]) for(j in 1:J) { mu.p[i,t,j] <- x[i,t]*p y[i,t,j] ~ dbern(mu.p[i,t,j]) } } } }